EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for tra...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2020-07, Vol.21 (7), p.2816-2827
Main Authors: Lan, Guohao, Xu, Weitao, Ma, Dong, Khalifa, Sara, Hassan, Mahbub, Hu, Wen
Format: Article
Language:English
Subjects:
Accelerometers
Biomedical monitoring
Energy
Energy consumption
Energy harvesting
Evaluation
Global Positioning System
Kinetic energy
Location based services
Monitoring
Sensors
sparse representation
Transportation
Transportation mode detection
Urban planning
wearable devices
Wearable technology
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Summary:Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2918642